The present invention relates to 3D object detection in images, and more particularly, to automated detection of 3D anatomical structures in medical images using marginal space learning.
Efficiently localizing anatomical structures (e.g., heart, liver, kidney, etc.) in medical images is often a prerequisite for further diagnostic image processing procedures, such as segmentation, measuring, and classification. Detecting and segmenting human anatomic structures in 3D medical image volumes (e.g., CT, MRI, etc.) is a challenging problem, which is typically more difficult than detecting anatomic structures in 2D images.
Previously, marginal space learning (MSL) has been proposed for efficient and automatic 3D object localization based on learning of discriminative classifiers. The full parameter space for 3D object localization has nine dimensions: three for position (Px, Py, and Pz), three for orientation (represented with Euler angles, ψ, φ, and θ), and three for anisotropic scaling (Sx, Sy, and Sz). In MSL, in order to efficiently localize an object, parameter estimation is performed in a series of marginal spaces with increasing dimensionality. In particular, the object detection is split into three steps: object position estimation, position-orientation estimation, and similarity transformation estimation. Each step results in a relatively small number of candidates, which are used in the following step. Accordingly, instead of uniformly searching the original nine-dimensional parameter space, low-dimensional marginal spaces are uniformly searched in MSL. MSL has been successfully applied to many 3D anatomical detection problems in medical imaging, such as ileocecal valves, polyps, and livers in abdominal CT, brain tissues and heart chambers in ultrasound images, and heart chambers in MRI.
MSL can reduce the number of testing hypotheses by approximately six orders of magnitude as compared with uniformly searching the nine-dimensional parameter space. However, in many cases MSL tests more testing hypotheses than necessary for accurate object detection. Accordingly, it is desirable to further increase the efficiency of anatomical object detection using MSL.